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Just for fun I thought it would be interesting to write a quick program to convert .jpg images to an ASCII representation. Here’s a sample output:

I was surprised that there are not more examples online showing how to use libjpeg, and even less wrapping it in a more cpp-friendly manner.

I decided to make the functionality re-usable, so if you have a need to read in a .jpg file, modify it, and save it, then this is going to be useful to you.

One point of interest, the shrink() member function – initially I wrote it very simply. I went through each (new) pixel in turn and calculated it as the average of a box of pixels in the original, larger, bitmap. This worked fine, but at the time I knew it was inefficient. Jumping around a surrounding box means cache misses galore. The best way to do this is one line at a time – this means we’re processing data already in processor cache lines, and is simpler for the CPU to do correct branch prediction. The version you see in the current source was my more considered version – it ended up three times faster than the initial version (and as it’s on github you can compare it to the old version in the history). I’ve left the getAverage() member function in, in case it is useful for anyone, but no longer use it in shrink().

If you’re on a linux distro you’ll undoubtedly have libjpeg (or one of its forks) installed. To use it and my source, you’ll need to install the header files for it, with:

sudo apt install libjpeg-dev

You can also install it on Windows, as a test I used Microsoft’s vcpkg to get the header files.

I came across this photo in my archive from quite a few years ago. I was taking one of those awful online C++ tests as an initial pre-interview screener. I took a photo of this question as an example of the standard of questions this particular company was asking.

What do you think the answer is?

When you’ve gone through a series of timed questions like this, you start to second-guess the person who wrote it.

Clearly the variable “o” is a stack-based variable, a pointer, so will be destroyed at the end of its scope. So technically the answer is “a”.

But I have a feeling the person that wrote this dubious question actually means to ask about what o points to, especially as it’s a memory leak (unless the mysterious bar() function deletes what o points to). I remember being in a quandary about this one… do you answer entirely correctly? Or do you try to answer the question you think is being asked?

Obviously in a face-to-face or telephone interview one could discuss this. I’ve always preferred, when being the interviewER, to sit down with someone in a real-world situation, give them access to Stack Overflow and Google (which, as we all know, are essential tools of the trade these days), and ask them to explain their thought processes.

If you’re turning down potential candidates on the strength of questions like the above, I’d argue you’re doing it wrong.

Note: this is an introduction to / overview of Boost MultiIndex. It is not intended to be in-depth.

So. Containers. In the words of luminaries in the C++ field such as Chandler Caruth, Scott Meyers and plenty of others, reach first for std::vector!

99% of the time this container, out of all the choices you have in the STL, is the right choice. The next most obvious is std::map if you need to “index” access to data: for example if you store 1,000,000 phone numbers and you want to quickly find one given a name, a std:map will give you “indexed” access – you look up your data in O(log n) time (or potentially quicker if you’re using a std::unordered_map) as opposed to O(n) time for an unsorted std::vector. (Obviously these are theoretical complexities – see a previous post which advocates actual benchmarking, as processor cache and branch prediction can have a huge effect).Continue reading “Getting Started with Boost MultiIndex”

I saw a question online recently which basically asked whether big-O notation always holds true in practice. For example, in theory, searching through a vector for an item should be O(n) whereas searching a map should be O(log n). Right?

Let’s imagine we create a vector with 100,000 random integers in it. We then sort the vector, and run two different searches on it. The first search starts at the beginning, compares the element against the value we’re searching for, and simply keeps incrementing until we find it. A naive search you might say. Without resorting to std:: algorithms, how might you improve it?

A worthy idea might be a recursive binary, or bisection, search. We might, for example, make 15 bisections to find a value in the upper quadrant of our range, compared to, say 75,000 comparisons in the brute force approach.

That’s fairly safe to say that the binary search is going to be much faster, right?

Are you surprised to see that the binary search took (very) slightly longer than the brute force approach?

Welcome to the world of modern processors. What’s going on there is a healthy dose of branch prediction and plenty of valid processor cache hits which actually makes the brute force approach viable.

I guess the moral of this story is, as always, don’t optimise prematurely – because you might actually be making things worse! Always profile your hot spots and work empirically rather than on what you think you know about algorithm efficiency.

The code used here is reproduced below – it should compile on VS 2015, clang and g++ without issue. You’ll need Boost for the timers. You may see wildly different timings depending on optimisation levels and other factors :)

C++11 gives us two useful indispensable smart pointers, std::unique_ptr and std::shared_ptr. So much has been written about these that there’s no point me re-hashing anything other than to re-iterate that if you are using “naked” owning pointers in your code these days, you are simply doing it wrong.

Here’s another little “gotcha” from my experimentations in running C# apps cross-platform. The following code implements a system-wide mutex to ensure that only one instance of the program is running at any one time:

public static void Main(string[] args){
Mutex mutex = null;
try{
mutex = Mutex.OpenExisting("test.martyndavis.com");
// if we *can* open it we're an ADDITIONAL instance
Console.WriteLine("Application is already running");
return;
} catch(WaitHandleCannotBeOpenedException){
// if we get in here then the mutex doesn't exist,
// so we are the only instance running on this machine
mutex = new System.Threading.Mutex(true, "test.martyndavis.com");
}
try{
Console.WriteLine("Doing stuff... press a key");
Console.Read();
} finally{
// Although mutex implements IDisposable, it doesn't
// call Release Mutex when being Disposed(),
// hence usage of try/finally instead of 'using'.
mutex.ReleaseMutex();
}
}

Running this on Windows it behaves as one would expect. Run one instance you get “Doing stuff… press a key” output, and, if you try to run a second instance in another DOS box you get “Application is already running”.

On Linux though it’s a different story – each instance runs as if it’s the only copy on the machine.

Doing some digging turns up this note in Mono’s release docs – you can see that shared handles are now turned off by default – you can turn them on by setting the environment variable MONO_ENABLE_SHM, but for the application I’m working on I think I’ll simply avoid mutexes and use something else to ensure one instance – a lock file in the application’s data directory will be simple, and work cross-platform.

As I’ve mentioned a couple of times before, I’ve been working on a C# application, using GTK# to enable me to create a GUI application which can run on Linux, Mac or Windows. It works well but I’ve been working through some “gotchas” here and there. Here’s a useful little tip… Gtk# is not a thread-safe toolkit.Continue reading “C# / GTK# – Re-Entrancy Issues”